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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
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Next issue: Feb 2017
Avg review time: 96 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  3/2016 - 4

Thermal Response Estimation in Substation Connectors Using Data-Driven Models

GIACOMETTO, F. See more information about GIACOMETTO, F. on SCOPUS See more information about GIACOMETTO, F. on IEEExplore See more information about GIACOMETTO, F. on Web of Science, CAPELLI, F. See more information about  CAPELLI, F. on SCOPUS See more information about  CAPELLI, F. on SCOPUS See more information about CAPELLI, F. on Web of Science, ROMERAL, L. See more information about  ROMERAL, L. on SCOPUS See more information about  ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on Web of Science, RIBA, J.-R. See more information about  RIBA, J.-R. on SCOPUS See more information about  RIBA, J.-R. on SCOPUS See more information about RIBA, J.-R. on Web of Science, SALA, E. See more information about SALA, E. on SCOPUS See more information about SALA, E. on SCOPUS See more information about SALA, E. on Web of Science
 
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Download PDF pdficon (1,247 KB) | Citation | Downloads: 136 | Views: 199

Author keywords
computer simulation, connectors, finite element methods, predictive models, thermal analysis

References keywords
engineer(8), comput(7), neural(6), jcie(6), indust(6), simulation(5), process(5), finite(5), element(5), time(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 25 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03004
Web of Science Accession Number: 000384750000004
SCOPUS ID: 84991096061

Abstract
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Temperature rise simulations are one of the key steps in the design of high-voltage substation connectors. These simulations help minimizing the number of experimental tests, which are power consuming and expensive. The conventional approach to perform these simulations relies on finite element method (FEM). It is highly desirable to reduce the number of required FEM simulations since they are time-consuming. To this end, this paper presents a data-driven modeling approach to drastically shorten the required simulation time. The data-driven approach estimates the thermal response of substation connectors from the data provided by a reduced number of FEM simulations of different operating conditions, thus allowing extrapolating the thermal response to other operating conditions. In the study, a partitioning method is also applied to enhance the performance of the learning stage of a set of data-driven methods, which are then compared and evaluated in terms of simulation time and accuracy to select the optimal configuration of the data-driven model. Finally, the complete methodology is validated against simulation tests.


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Copyright ©2001-2016
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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